Abstract
Deep learning has transformed visual data analysis, with Convolutional Neural Networks (CNNs) becoming highly effective in learning meaningful feature representations directly from images. Unlike traditional manual feature engineering methods, CNNs automatically extract hierarchical visual patterns, enabling strong performance across diverse real-world contexts. This study investigates the effectiveness of CNN-based architectures across five heterogeneous datasets spanning agricultural and urban domains: mango variety classification, paddy variety identification, road surface condition assessment, auto-rickshaw detection, and footpath encroachment monitoring. These datasets introduce varying challenges, including differences in illumination, resolution, environmental complexity, and class imbalance, necessitating adaptable and robust learning models. We evaluate a lightweight, task-specific custom CNN alongside established deep architectures, including ResNet-18 and VGG-16, trained both from scratch and using transfer learning. Through systematic preprocessing, augmentation, and controlled experimentation, we analyze how architectural complexity, model depth, and pre-training influence convergence, generalization, and performance across datasets of differing scale and difficulty. The key contributions of this work are: (1) the development of an efficient custom CNN that achieves competitive performance across multiple application domains, and (2) a comprehensive comparative analysis highlighting when transfer learning and deep architectures provide substantial advantages, particularly in data-constrained environments. These findings offer practical insights for deploying deep learning models in resource-limited yet high-impact real-world visual classification tasks.
Abstract (translated)
深度学习已经彻底改变了视觉数据分析，卷积神经网络（CNN）尤其擅长直接从图像中学习有意义的特征表示。与传统的手动特征工程方法不同，CNN能够自动提取分层的视觉模式，在各种现实世界的背景下表现出强大的性能。本研究探讨了基于CNN架构在五个异构数据集上的有效性，这些数据集涵盖了农业和城市领域：芒果品种分类、水稻品种识别、路面状况评估、机动三轮车检测以及人行道侵占监控。这些数据集引入了不同的挑战，包括光照条件的变化、分辨率差异、环境复杂性和类别不平衡等问题，这需要灵活且鲁棒的学习模型。我们评估了一个轻量级的任务特定自定义CNN，并与现有的深度架构进行比较，包括从头开始训练和使用迁移学习的ResNet-18和VGG-16。通过系统的预处理、增强和控制实验，我们分析了架构复杂性、模型深度以及预训练如何影响不同规模和难度的数据集的收敛性、泛化性和性能。 这项工作的主要贡献包括：（1）开发了一种高效的定制CNN，在多个应用领域中实现了竞争性的表现；（2）通过全面比较分析展示了在数据有限环境下，迁移学习和深层架构提供了显著的优势。这些发现为在资源受限但具有重要影响的现实世界视觉分类任务中部署深度学习模型提供了解决方案的实际见解。
URL
https://arxiv.org/abs/2601.04727
